(146c) Magnetic Resonance Imaging of Wet Fluidization | AIChE

(146c) Magnetic Resonance Imaging of Wet Fluidization


Penn, A., ETH Zurich
Pruessmann, K. P., ETH Zurich and University of Zurich
In the energy, pharmaceuticals and polymer production industries, it is common for small amounts of liquid to be injected into gas-solid fluidized beds. In some cases, the liquid is a reactant, while in others it facilitates agglomeration or heat transfer. The liquid leaves to the formation of cohesive liquid bridges between particles as well as between particles and walls, often having significant effects on hydrodynamics. These effects include formation of agglomerates and in certain cases can lead to local or device-scale defluidization.

Despite the industrial importance of liquid bridging on gas-solid fluidization, the effect of liquid on fluidization hydrodynamics is still not well understood. A small number of previous experimental studies have demonstrated effects of liquid on bed on bed fluidity1,2, particle velocities3–6, bed height1, bubble size6,7 and minimum fluidization velocity6. Additionally, various computational models have been developed and utilized to understand the effects of liquid bridging on fluidization hydrodynamics. Some studies have simply lowered the coefficient of restitution8,9 to account for the effects of liquid bridging, while others have modeled the capillary forces in the normal direction10,11, the viscous forces in the normal and tangential directions12, as well as the rate of liquid transfer between the surfaces of particles and liquid bridges13. Due to a lack of detailed experimental studies of a variety of hydrodynamic features under a variety of liquid conditions, the current literature does not fully elucidate the relative importance of liquid loading, surface tension and viscosity on fluidization behavior. This absence in the experimental literature leads to issues in understanding the validity and areas for improvement in existing computational models.

Previously, MRI has been used to study hydrodynamics in detail in gas-solid fluidized beds. Here, we use MRI to image local particle concentration and velocity with high temporal resolution in a 3D freely bubbling fluidized bed. We use these measurements to characterize both average values and variations in bubble size, bubble number density and particle speed under varying gas flow rates, liquid loading conditions, and values of surface tension and viscosity. We use these results to evaluate the importance and validity of different computational sub-models for liquid bridging.


1. McDougall SL, Saberian M, Briens C, Berruti F, Chan EW. Characterization of Fluidization Quality in Fluidized Beds of Wet Particles. Int J Chem React Eng. 2004;2(1).

2. McDougall S, Saberian M, Briens C, Berruti F, Chan E. Using dynamic pressure signals to assess the effects of injected liquid on fluidized bed properties. Chem Eng Process Process Intensif. 2005;44(7):701-708.

3. Sutkar VS, Deen NG, Patil AV, et al. Experimental study of hydrodynamics and thermal behavior of a pseudo-2D spout-fluidized bed with liquid injection. AIChE J. 2015;61(4):1146-1159.

4. Zhang Q, Zhou Y, Wang J, et al. Particle Motion in Two- and Three-Phase Fluidized-Bed Reactors Determined by Pulsed Field Gradient Nuclear Magnetic Resonance. Chem Eng Technol. June 2015:n/a-n/a.

5. Passos ML, Mujumdar AS. Effect of cohesive forces on fluidized and spouted beds of wet particles. Powder Technol. 2000;110(3):222-238.

6. Zhou Y, Ren C, Wang J, Yang Y. Characterization on hydrodynamic behavior in liquid-containing gas-solid fluidized bed reactor. AIChE J. 2013;59(4):1056-1065.

7. Ma J, Liu D, Chen X. Bubble Behaviors of Large Cohesive Particles in a 2D Fluidized Bed. Ind Eng Chem Res. 2016;55(3):624-634.

8. Darabi P, Pougatch K, Salcudean M, Grecov D. DEM investigations of fluidized beds in the presence of liquid coating. Powder Technol. 2011;214(3):365-374.

9. Sutkar VS, Deen NG, Padding JT, et al. A novel approach to determine wet restitution coefficients through a unified correlation and energy analysis. AIChE J. 2015;61(3):769-779.

10. Mikami T, Kamiya H, Horio M. Numerical simulation of cohesive powder behavior in a fluidized bed. Chem Eng Sci. 1998;53(10):1927-1940.

11. Girardi M, Radl S, Sundaresan S. Simulating wet gas–solid fluidized beds using coarse-grid CFD-DEM. Chem Eng Sci. 2016;144:224-238.

12. Kuwagi K, Mikami T, Horio M. Numerical simulation of metallic solid bridging particles in a fluidized bed at high temperature. Powder Technol. 2000;109(1–3):27-40.

13. Wu M, Radl S, Khinast JG. A model to predict liquid bridge formation between wet particles based on direct numerical simulations. AIChE J. 2016;62(6):1877-1897.